Overview

Dataset statistics

Number of variables40
Number of observations608161
Missing cells14544217
Missing cells (%)59.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory185.6 MiB
Average record size in memory320.0 B

Variable types

Categorical37
Numeric3

Alerts

url has a high cardinality: 6600 distinct values High cardinality
date has a high cardinality: 1060 distinct values High cardinality
awayplay has a high cardinality: 111977 distinct values High cardinality
awayscore has a high cardinality: 542 distinct values High cardinality
hometeam has a high cardinality: 162 distinct values High cardinality
homeplay has a high cardinality: 108871 distinct values High cardinality
homescore has a high cardinality: 544 distinct values High cardinality
shooter has a high cardinality: 1113 distinct values High cardinality
assister has a high cardinality: 878 distinct values High cardinality
blocker has a high cardinality: 742 distinct values High cardinality
fouler has a high cardinality: 944 distinct values High cardinality
fouled has a high cardinality: 867 distinct values High cardinality
rebounder has a high cardinality: 936 distinct values High cardinality
violationplayer has a high cardinality: 346 distinct values High cardinality
freethrowshooter has a high cardinality: 873 distinct values High cardinality
entergame has a high cardinality: 969 distinct values High cardinality
leavegame has a high cardinality: 887 distinct values High cardinality
turnoverplayer has a high cardinality: 845 distinct values High cardinality
turnovertype has a high cardinality: 453 distinct values High cardinality
turnovercauser has a high cardinality: 766 distinct values High cardinality
jumpballawayplayer has a high cardinality: 559 distinct values High cardinality
jumpballhomeplayer has a high cardinality: 398 distinct values High cardinality
jumpballposs has a high cardinality: 473 distinct values High cardinality
location is highly correlated with time and 2 other fieldsHigh correlation
time is highly correlated with location and 2 other fieldsHigh correlation
winningteam is highly correlated with location and 3 other fieldsHigh correlation
awayteam is highly correlated with winningteam and 1 other fieldsHigh correlation
shottype is highly correlated with shotdistHigh correlation
shotdist is highly correlated with shottypeHigh correlation
timeoutteam is highly correlated with location and 3 other fieldsHigh correlation
awayplay has 301043 (49.5%) missing values Missing
homeplay has 305494 (50.2%) missing values Missing
shooter has 376103 (61.8%) missing values Missing
shottype has 379466 (62.4%) missing values Missing
shotoutcome has 379466 (62.4%) missing values Missing
shotdist has 379466 (62.4%) missing values Missing
assister has 546664 (89.9%) missing values Missing
blocker has 595235 (97.9%) missing values Missing
foultype has 552823 (90.9%) missing values Missing
fouler has 552823 (90.9%) missing values Missing
fouled has 556053 (91.4%) missing values Missing
rebounder has 470175 (77.3%) missing values Missing
reboundtype has 470175 (77.3%) missing values Missing
violationplayer has 605996 (99.6%) missing values Missing
violationtype has 605996 (99.6%) missing values Missing
timeoutteam has 592310 (97.4%) missing values Missing
freethrowshooter has 547711 (90.1%) missing values Missing
freethrowoutcome has 547711 (90.1%) missing values Missing
freethrownum has 547711 (90.1%) missing values Missing
entergame has 547406 (90.0%) missing values Missing
leavegame has 547406 (90.0%) missing values Missing
turnoverplayer has 574146 (94.4%) missing values Missing
turnovertype has 570756 (93.8%) missing values Missing
turnovercause has 587928 (96.7%) missing values Missing
turnovercauser has 587928 (96.7%) missing values Missing
jumpballawayplayer has 602617 (99.1%) missing values Missing
jumpballhomeplayer has 605980 (99.6%) missing values Missing
jumpballposs has 605980 (99.6%) missing values Missing
secleft has 12196 (2.0%) zeros Zeros
shotdist has 14631 (2.4%) zeros Zeros

Reproduction

Analysis started2022-01-05 23:21:09.209276
Analysis finished2022-01-05 23:24:11.027799
Duration3 minutes and 1.82 second
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

url
Categorical

HIGH CARDINALITY

Distinct6600
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
/boxscores/201611230PHI.html
 
140
/boxscores/201510300ORL.html
 
136
/boxscores/201905030POR.html
 
130
/boxscores/201812260BRK.html
 
130
/boxscores/202001100WAS.html
 
129
Other values (6595)
607496 

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row/boxscores/202012220BRK.html
2nd row/boxscores/202012220BRK.html
3rd row/boxscores/202012220BRK.html
4th row/boxscores/202012220BRK.html
5th row/boxscores/202012220BRK.html

Common Values

ValueCountFrequency (%)
/boxscores/201611230PHI.html140
 
< 0.1%
/boxscores/201510300ORL.html136
 
< 0.1%
/boxscores/201905030POR.html130
 
< 0.1%
/boxscores/201812260BRK.html130
 
< 0.1%
/boxscores/202001100WAS.html129
 
< 0.1%
/boxscores/202001040SAC.html129
 
< 0.1%
/boxscores/201703230BRK.html129
 
< 0.1%
/boxscores/201701290ATL.html129
 
< 0.1%
/boxscores/201612280ATL.html129
 
< 0.1%
/boxscores/201512300SAC.html128
 
< 0.1%
Other values (6590)606852
99.8%

Length

2022-01-05T15:24:11.168939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boxscores/201611230phi.html140
 
< 0.1%
boxscores/201510300orl.html136
 
< 0.1%
boxscores/201905030por.html130
 
< 0.1%
boxscores/201812260brk.html130
 
< 0.1%
boxscores/202001100was.html129
 
< 0.1%
boxscores/202001040sac.html129
 
< 0.1%
boxscores/201703230brk.html129
 
< 0.1%
boxscores/201701290atl.html129
 
< 0.1%
boxscores/201612280atl.html129
 
< 0.1%
boxscores/201512300sac.html128
 
< 0.1%
Other values (6590)606852
99.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gametype
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
regular
570400 
playoff
 
37761

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular570400
93.8%
playoff37761
 
6.2%

Length

2022-01-05T15:24:11.310852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:11.394343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
regular570400
93.8%
playoff37761
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

location
Categorical

HIGH CORRELATION

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
STAPLES Center Los Angeles California
 
38959
TD Garden Boston Massachusetts
 
21366
Toyota Center Houston Texas
 
21205
Quicken Loans Arena Cleveland Ohio
 
21010
Moda Center Portland Oregon
 
20511
Other values (42)
485110 

Length

Max length49
Median length36
Mean length35.82076128
Min length26

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBarclays Center Brooklyn New York
2nd rowBarclays Center Brooklyn New York
3rd rowBarclays Center Brooklyn New York
4th rowBarclays Center Brooklyn New York
5th rowBarclays Center Brooklyn New York

Common Values

ValueCountFrequency (%)
STAPLES Center Los Angeles California38959
 
6.4%
TD Garden Boston Massachusetts21366
 
3.5%
Toyota Center Houston Texas21205
 
3.5%
Quicken Loans Arena Cleveland Ohio21010
 
3.5%
Moda Center Portland Oregon20511
 
3.4%
Chesapeake Energy Arena Oklahoma City Oklahoma20509
 
3.4%
Wells Fargo Center Philadelphia Pennsylvania20470
 
3.4%
FedEx Forum Memphis Tennessee19593
 
3.2%
AT&T Center San Antonio Texas19530
 
3.2%
Barclays Center Brooklyn New York19415
 
3.2%
Other values (37)385593
63.4%

Length

2022-01-05T15:24:11.482490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
center307261
 
10.0%
arena187410
 
6.1%
california80518
 
2.6%
new75802
 
2.5%
texas59467
 
1.9%
york56699
 
1.8%
florida54219
 
1.8%
city41507
 
1.4%
oklahoma41018
 
1.3%
garden40008
 
1.3%
Other values (135)2125131
69.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date
Categorical

HIGH CARDINALITY

Distinct1060
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
November 25 2016
 
1406
October 28 2015
 
1405
November 27 2019
 
1317
December 28 2019
 
1309
April 12 2017
 
1303
Other values (1055)
601421 

Length

Max length17
Median length15
Mean length14.58722772
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDecember 22 2020
2nd rowDecember 22 2020
3rd rowDecember 22 2020
4th rowDecember 22 2020
5th rowDecember 22 2020

Common Values

ValueCountFrequency (%)
November 25 20161406
 
0.2%
October 28 20151405
 
0.2%
November 27 20191317
 
0.2%
December 28 20191309
 
0.2%
April 12 20171303
 
0.2%
November 21 20181300
 
0.2%
January 20 20201291
 
0.2%
April 7 20191291
 
0.2%
November 29 20191290
 
0.2%
February 19 20161273
 
0.2%
Other values (1050)594976
97.8%

Length

2022-01-05T15:24:11.647749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016122088
 
6.7%
2017121946
 
6.7%
2018120233
 
6.6%
2019119335
 
6.5%
january115621
 
6.3%
december109515
 
6.0%
november101423
 
5.6%
march91798
 
5.0%
february75883
 
4.2%
202066345
 
3.6%
Other values (40)780296
42.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

time
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
8:00 PM
138649 
7:00 PM
111986 
7:30 PM
85335 
10:30 PM
57910 
9:00 PM
55259 
Other values (20)
159022 

Length

Max length8
Median length7
Mean length7.153995406
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7:00 PM
2nd row7:00 PM
3rd row7:00 PM
4th row7:00 PM
5th row7:00 PM

Common Values

ValueCountFrequency (%)
8:00 PM138649
22.8%
7:00 PM111986
18.4%
7:30 PM85335
14.0%
10:30 PM57910
9.5%
9:00 PM55259
 
9.1%
8:30 PM38250
 
6.3%
10:00 PM32322
 
5.3%
6:00 PM21328
 
3.5%
3:30 PM16209
 
2.7%
9:30 PM13313
 
2.2%
Other values (15)37600
 
6.2%

Length

2022-01-05T15:24:11.785270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm608161
50.0%
8:00138649
 
11.4%
7:00111986
 
9.2%
7:3085335
 
7.0%
10:3057910
 
4.8%
9:0055259
 
4.5%
8:3038250
 
3.1%
10:0032322
 
2.7%
6:0021328
 
1.8%
3:3016209
 
1.3%
Other values (16)50913
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

winningteam
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
GSW
 
31230
TOR
 
29432
BOS
 
27496
HOU
 
26946
LAC
 
25348
Other values (25)
467709 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRK
2nd rowBRK
3rd rowBRK
4th rowBRK
5th rowBRK

Common Values

ValueCountFrequency (%)
GSW31230
 
5.1%
TOR29432
 
4.8%
BOS27496
 
4.5%
HOU26946
 
4.4%
LAC25348
 
4.2%
SAS25250
 
4.2%
OKC25192
 
4.1%
MIL24722
 
4.1%
UTA23416
 
3.9%
POR22541
 
3.7%
Other values (20)346588
57.0%

Length

2022-01-05T15:24:11.909031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gsw31230
 
5.1%
tor29432
 
4.8%
bos27496
 
4.5%
hou26946
 
4.4%
lac25348
 
4.2%
sas25250
 
4.2%
okc25192
 
4.1%
mil24722
 
4.1%
uta23416
 
3.9%
por22541
 
3.7%
Other values (20)346588
57.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

quarter
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.542834874
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2022-01-05T15:24:12.233700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.138156343
Coefficient of variation (CV)0.447593493
Kurtosis-1.203029441
Mean2.542834874
Median Absolute Deviation (MAD)1
Skewness0.0385575324
Sum1546453
Variance1.295399862
MonotonicityNot monotonic
2022-01-05T15:24:12.350097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4156497
25.7%
2154211
25.4%
3147424
24.2%
1145278
23.9%
54168
 
0.7%
6474
 
0.1%
763
 
< 0.1%
846
 
< 0.1%
ValueCountFrequency (%)
1145278
23.9%
2154211
25.4%
3147424
24.2%
4156497
25.7%
54168
 
0.7%
6474
 
0.1%
763
 
< 0.1%
846
 
< 0.1%
ValueCountFrequency (%)
846
 
< 0.1%
763
 
< 0.1%
6474
 
0.1%
54168
 
0.7%
4156497
25.7%
3147424
24.2%
2154211
25.4%
1145278
23.9%

secleft
Real number (ℝ≥0)

ZEROS

Distinct721
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.7428247
Minimum0
Maximum720
Zeros12196
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2022-01-05T15:24:12.501590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q1152
median325
Q3506
95-th percentile667
Maximum720
Range720
Interquartile range (IQR)354

Descriptive statistics

Standard deviation207.5229994
Coefficient of variation (CV)0.6274452049
Kurtosis-1.169206404
Mean330.7428247
Median Absolute Deviation (MAD)177
Skewness0.08676519337
Sum201144887
Variance43065.79528
MonotonicityNot monotonic
2022-01-05T15:24:12.680474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012196
 
2.0%
12800
 
0.5%
21976
 
0.3%
31652
 
0.3%
7201426
 
0.2%
41397
 
0.2%
51363
 
0.2%
301300
 
0.2%
321293
 
0.2%
331290
 
0.2%
Other values (711)581468
95.6%
ValueCountFrequency (%)
012196
2.0%
12800
 
0.5%
21976
 
0.3%
31652
 
0.3%
41397
 
0.2%
51363
 
0.2%
61207
 
0.2%
71197
 
0.2%
81009
 
0.2%
9959
 
0.2%
ValueCountFrequency (%)
7201426
0.2%
7194
 
< 0.1%
7185
 
< 0.1%
7177
 
< 0.1%
71611
 
< 0.1%
71516
 
< 0.1%
71426
 
< 0.1%
71348
 
< 0.1%
71288
 
< 0.1%
711149
 
< 0.1%

awayteam
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
GSW
 
22417
BOS
 
22195
TOR
 
21957
OKC
 
21605
HOU
 
21362
Other values (25)
498625 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGSW
2nd rowGSW
3rd rowGSW
4th rowGSW
5th rowGSW

Common Values

ValueCountFrequency (%)
GSW22417
 
3.7%
BOS22195
 
3.6%
TOR21957
 
3.6%
OKC21605
 
3.6%
HOU21362
 
3.5%
POR21313
 
3.5%
PHI21134
 
3.5%
UTA21010
 
3.5%
CLE20977
 
3.4%
MIL20923
 
3.4%
Other values (20)393268
64.7%

Length

2022-01-05T15:24:12.832994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gsw22417
 
3.7%
bos22195
 
3.6%
tor21957
 
3.6%
okc21605
 
3.6%
hou21362
 
3.5%
por21313
 
3.5%
phi21134
 
3.5%
uta21010
 
3.5%
cle20977
 
3.4%
mil20923
 
3.4%
Other values (20)393268
64.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

awayplay
Categorical

HIGH CARDINALITY
MISSING

Distinct111977
Distinct (%)36.5%
Missing301043
Missing (%)49.5%
Memory size4.6 MiB
Offensive rebound by Team
 
7946
Defensive rebound by Team
 
2807
End of 3rd quarter
 
1342
End of Game
 
1324
End of 1st quarter
 
1308
Other values (111972)
292391 

Length

Max length82
Median length39
Mean length39.78287824
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81752 ?
Unique (%)26.6%

Sample

1st rowTurnover by K. Oubre (offensive foul)
2nd rowS. Curry misses 3-pt jump shot from 25 ft
3rd rowS. Curry makes 2-pt jump shot from 23 ft
4th rowOffensive rebound by K. Oubre
5th rowOffensive rebound by K. Oubre

Common Values

ValueCountFrequency (%)
Offensive rebound by Team7946
 
1.3%
Defensive rebound by Team2807
 
0.5%
End of 3rd quarter1342
 
0.2%
End of Game1324
 
0.2%
End of 1st quarter1308
 
0.2%
End of 2nd quarter1271
 
0.2%
End of 4th quarter1080
 
0.2%
Official timeout820
 
0.1%
Turnover by Team (shot clock)731
 
0.1%
Defensive rebound by A. Drummond420
 
0.1%
Other values (111967)288069
47.4%
(Missing)301043
49.5%

Length

2022-01-05T15:24:13.018872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
by186112
 
7.7%
from107288
 
4.4%
ft107207
 
4.4%
shot79331
 
3.3%
jump76866
 
3.2%
2-pt75435
 
3.1%
makes74479
 
3.1%
misses69344
 
2.9%
rebound68073
 
2.8%
j58091
 
2.4%
Other values (1009)1513560
62.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

awayscore
Categorical

HIGH CARDINALITY

Distinct542
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0
 
9287
2
 
7577
17
 
6000
25
 
5971
23
 
5955
Other values (537)
573371 

Length

Max length32
Median length2
Mean length2.058255955
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row4
3rd row4
4th row6
5th row8

Common Values

ValueCountFrequency (%)
09287
 
1.5%
27577
 
1.2%
176000
 
1.0%
255971
 
1.0%
235955
 
1.0%
325926
 
1.0%
815911
 
1.0%
265896
 
1.0%
435891
 
1.0%
535888
 
1.0%
Other values (532)543859
89.4%

Length

2022-01-05T15:24:13.205392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
09287
 
1.5%
27577
 
1.2%
176000
 
1.0%
255971
 
1.0%
235955
 
1.0%
325926
 
1.0%
815911
 
1.0%
265896
 
1.0%
435891
 
1.0%
535888
 
1.0%
Other values (510)548806
89.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hometeam
Categorical

HIGH CARDINALITY

Distinct162
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
GSW
 
22873
TOR
 
22652
BOS
 
22427
HOU
 
22091
LAC
 
21232
Other values (157)
496886 

Length

Max length3
Median length3
Mean length2.997332943
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowBRK
2nd rowBRK
3rd rowBRK
4th rowBRK
5th rowBRK

Common Values

ValueCountFrequency (%)
GSW22873
 
3.8%
TOR22652
 
3.7%
BOS22427
 
3.7%
HOU22091
 
3.6%
LAC21232
 
3.5%
OKC21146
 
3.5%
POR21107
 
3.5%
PHI21069
 
3.5%
MIL20974
 
3.4%
CLE20962
 
3.4%
Other values (152)391628
64.4%

Length

2022-01-05T15:24:13.348847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gsw22873
 
3.8%
tor22652
 
3.7%
bos22427
 
3.7%
hou22091
 
3.6%
lac21232
 
3.5%
okc21146
 
3.5%
por21107
 
3.5%
phi21069
 
3.5%
mil20974
 
3.4%
cle20962
 
3.4%
Other values (152)391628
64.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

homeplay
Categorical

HIGH CARDINALITY
MISSING

Distinct108871
Distinct (%)36.0%
Missing305494
Missing (%)50.2%
Memory size4.6 MiB
Offensive rebound by Team
 
8116
Defensive rebound by Team
 
2793
Turnover by Team (shot clock)
 
744
Defensive rebound by D. Green
 
451
Defensive rebound by D. Jordan
 
419
Other values (108866)
290144 

Length

Max length79
Median length39
Mean length39.97962447
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78426 ?
Unique (%)25.9%

Sample

1st rowK. Irving makes 2-pt jump shot from 22 ft (assist by K. Durant)
2nd rowDefensive rebound by K. Irving
3rd rowS. Dinwiddie misses 3-pt jump shot from 26 ft
4th rowK. Irving misses 3-pt jump shot from 25 ft
5th rowOffensive rebound by Team

Common Values

ValueCountFrequency (%)
Offensive rebound by Team8116
 
1.3%
Defensive rebound by Team2793
 
0.5%
Turnover by Team (shot clock)744
 
0.1%
Defensive rebound by D. Green451
 
0.1%
Defensive rebound by D. Jordan419
 
0.1%
Defensive rebound by A. Drummond378
 
0.1%
Defensive rebound by G. Antetokounmpo372
 
0.1%
Defensive rebound by M. Morris364
 
0.1%
Defensive rebound by R. Gobert357
 
0.1%
Defensive rebound by H. Whiteside346
 
0.1%
Other values (108861)288327
47.4%
(Missing)305494
50.2%

Length

2022-01-05T15:24:13.542215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
by190493
 
8.0%
from106934
 
4.5%
ft106857
 
4.5%
shot78052
 
3.3%
makes76560
 
3.2%
2-pt76043
 
3.2%
jump73387
 
3.1%
rebound69913
 
2.9%
misses68776
 
2.9%
j58040
 
2.4%
Other values (1023)1488578
62.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

homescore
Categorical

HIGH CARDINALITY

Distinct544
Distinct (%)0.1%
Missing1649
Missing (%)0.3%
Memory size4.6 MiB
0
 
8871
2
 
7160
36
 
5886
39
 
5848
40
 
5843
Other values (539)
572904 

Length

Max length32
Median length2
Mean length2.079709882
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row10
3rd row10
4th row10
5th row13

Common Values

ValueCountFrequency (%)
08871
 
1.5%
27160
 
1.2%
365886
 
1.0%
395848
 
1.0%
405843
 
1.0%
205839
 
1.0%
315834
 
1.0%
185798
 
1.0%
295797
 
1.0%
755793
 
1.0%
Other values (534)543843
89.4%

Length

2022-01-05T15:24:13.725497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08871
 
1.5%
27160
 
1.2%
365886
 
1.0%
395848
 
1.0%
405843
 
1.0%
205839
 
1.0%
315834
 
1.0%
185798
 
0.9%
295797
 
0.9%
755793
 
0.9%
Other values (514)548989
89.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

shooter
Categorical

HIGH CARDINALITY
MISSING

Distinct1113
Distinct (%)0.5%
Missing376103
Missing (%)61.8%
Memory size4.6 MiB
J. Harden - hardeja01
 
1859
R. Westbrook - westbru01
 
1761
L. James - jamesle01
 
1751
D. Lillard - lillada01
 
1651
C. McCollum - mccolcj01
 
1584
Other values (1108)
223452 

Length

Max length33
Median length21
Mean length21.18468659
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)< 0.1%

Sample

1st rowK. Irving - irvinky01
2nd rowS. Dinwiddie - dinwisp01
3rd rowK. Irving - irvinky01
4th rowS. Curry - curryst01
5th rowJ. Harris - harrijo01

Common Values

ValueCountFrequency (%)
J. Harden - hardeja011859
 
0.3%
R. Westbrook - westbru011761
 
0.3%
L. James - jamesle011751
 
0.3%
D. Lillard - lillada011651
 
0.3%
C. McCollum - mccolcj011584
 
0.3%
D. DeRozan - derozde011535
 
0.3%
P. George - georgpa011445
 
0.2%
B. Beal - bealbr011440
 
0.2%
K. Walker - walkeke021427
 
0.2%
A. Davis - davisan021390
 
0.2%
Other values (1103)216215
35.6%
(Missing)376103
61.8%

Length

2022-01-05T15:24:13.875616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
228695
24.9%
j34303
 
3.7%
d28722
 
3.1%
k19511
 
2.1%
t17387
 
1.9%
m17387
 
1.9%
a13940
 
1.5%
r11960
 
1.3%
b11108
 
1.2%
c10730
 
1.2%
Other values (1879)524851
57.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

shottype
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)< 0.1%
Missing379466
Missing (%)62.4%
Memory size4.6 MiB
3-pt jump shot
77220 
2-pt jump shot
70607 
2-pt layup
60628 
2-pt dunk
12167 
2-pt hook shot
8069 
Other values (3)
 
4

Length

Max length14
Median length14
Mean length12.67352588
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2-pt jump shot
2nd row3-pt jump shot
3rd row3-pt jump shot
4th row3-pt jump shot
5th row3-pt jump shot

Common Values

ValueCountFrequency (%)
3-pt jump shot77220
 
12.7%
2-pt jump shot70607
 
11.6%
2-pt layup60628
 
10.0%
2-pt dunk12167
 
2.0%
2-pt hook shot8069
 
1.3%
3-pt layup2
 
< 0.1%
2-pt tip-in1
 
< 0.1%
3-pt hook shot1
 
< 0.1%
(Missing)379466
62.4%

Length

2022-01-05T15:24:14.017097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:14.135128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
shot155897
25.4%
2-pt151472
24.7%
jump147827
24.1%
3-pt77223
12.6%
layup60630
 
9.9%
dunk12167
 
2.0%
hook8070
 
1.3%
tip-in1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

shotoutcome
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing379466
Missing (%)62.4%
Memory size4.6 MiB
miss
124039 
make
104656 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmake
2nd rowmiss
3rd rowmiss
4th rowmiss
5th rowmiss

Common Values

ValueCountFrequency (%)
miss124039
 
20.4%
make104656
 
17.2%
(Missing)379466
62.4%

Length

2022-01-05T15:24:14.240069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:14.318826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
miss124039
54.2%
make104656
45.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

shotdist
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct88
Distinct (%)< 0.1%
Missing379466
Missing (%)62.4%
Infinite0
Infinite (%)0.0%
Mean13.32656158
Minimum0
Maximum87
Zeros14631
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2022-01-05T15:24:14.414138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q324
95-th percentile27
Maximum87
Range87
Interquartile range (IQR)22

Descriptive statistics

Standard deviation10.44787954
Coefficient of variation (CV)0.7839891394
Kurtosis-0.5406191373
Mean13.32656158
Median Absolute Deviation (MAD)11
Skewness0.2586792548
Sum3047718
Variance109.1581869
MonotonicityNot monotonic
2022-01-05T15:24:14.580404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125326
 
4.2%
2522112
 
3.6%
221277
 
3.5%
2616689
 
2.7%
014631
 
2.4%
2413038
 
2.1%
2310548
 
1.7%
310027
 
1.6%
276917
 
1.1%
46348
 
1.0%
Other values (78)81782
 
13.4%
(Missing)379466
62.4%
ValueCountFrequency (%)
014631
2.4%
125326
4.2%
221277
3.5%
310027
 
1.6%
46348
 
1.0%
55375
 
0.9%
65161
 
0.8%
74509
 
0.7%
84284
 
0.7%
93773
 
0.6%
ValueCountFrequency (%)
871
 
< 0.1%
864
< 0.1%
852
 
< 0.1%
842
 
< 0.1%
839
< 0.1%
828
< 0.1%
819
< 0.1%
805
< 0.1%
794
< 0.1%
786
< 0.1%

assister
Categorical

HIGH CARDINALITY
MISSING

Distinct878
Distinct (%)1.4%
Missing546664
Missing (%)89.9%
Memory size4.6 MiB
R. Westbrook - westbru01
 
833
J. Harden - hardeja01
 
776
L. James - jamesle01
 
750
R. Rubio - rubiori01
 
602
D. Green - greendr01
 
602
Other values (873)
57934 

Length

Max length33
Median length21
Mean length21.39164512
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)0.1%

Sample

1st rowK. Durant - duranke01
2nd rowS. Curry - curryst01
3rd rowS. Curry - curryst01
4th rowJ. Toscano-Anderson - toscaju01
5th rowK. Bazemore - bazemke01

Common Values

ValueCountFrequency (%)
R. Westbrook - westbru01833
 
0.1%
J. Harden - hardeja01776
 
0.1%
L. James - jamesle01750
 
0.1%
R. Rubio - rubiori01602
 
0.1%
D. Green - greendr01602
 
0.1%
K. Lowry - lowryky01600
 
0.1%
D. Lillard - lillada01576
 
0.1%
C. Paul - paulch01574
 
0.1%
R. Rondo - rondora01545
 
0.1%
J. Wall - walljo01510
 
0.1%
Other values (868)55129
 
9.1%
(Missing)546664
89.9%

Length

2022-01-05T15:24:14.754113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
61497
25.0%
j9323
 
3.8%
d8452
 
3.4%
k4722
 
1.9%
t4663
 
1.9%
m4662
 
1.9%
r4101
 
1.7%
a3100
 
1.3%
b2861
 
1.2%
c2688
 
1.1%
Other values (1576)139992
56.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

blocker
Categorical

HIGH CARDINALITY
MISSING

Distinct742
Distinct (%)5.7%
Missing595235
Missing (%)97.9%
Memory size4.6 MiB
R. Gobert - goberru01
 
189
H. Whiteside - whiteha01
 
179
M. Turner - turnemy01
 
178
A. Davis - davisan02
 
168
B. Lopez - lopezbr01
 
145
Other values (737)
12067 

Length

Max length33
Median length21
Mean length21.40345041
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)1.0%

Sample

1st rowJ. Allen - allenja01
2nd rowL. Kennard - kennalu01
3rd rowT. Rozier - roziete01
4th rowC. Zeller - zelleco01
5th rowJ. McGee - mcgeeja01

Common Values

ValueCountFrequency (%)
R. Gobert - goberru01189
 
< 0.1%
H. Whiteside - whiteha01179
 
< 0.1%
M. Turner - turnemy01178
 
< 0.1%
A. Davis - davisan02168
 
< 0.1%
B. Lopez - lopezbr01145
 
< 0.1%
G. Antetokounmpo - antetgi01134
 
< 0.1%
S. Ibaka - ibakase01127
 
< 0.1%
C. Capela - capelca01123
 
< 0.1%
D. Green - greendr01119
 
< 0.1%
L. Aldridge - aldrila01119
 
< 0.1%
Other values (732)11445
 
1.9%
(Missing)595235
97.9%

Length

2022-01-05T15:24:14.910973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12926
25.0%
j2018
 
3.9%
d1511
 
2.9%
m1266
 
2.4%
a1094
 
2.1%
k1029
 
2.0%
t783
 
1.5%
r739
 
1.4%
b565
 
1.1%
c558
 
1.1%
Other values (1345)29259
56.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

foultype
Categorical

MISSING

Distinct14
Distinct (%)< 0.1%
Missing552823
Missing (%)90.9%
Memory size4.6 MiB
shooting
24871 
personal
18183 
offensive
3810 
loose ball
3192 
personal take
 
1647
Other values (9)
3635 

Length

Max length16
Median length8
Mean length8.648866963
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshooting
2nd rowoffensive
3rd rowloose ball
4th rowshooting
5th rowpersonal

Common Values

ValueCountFrequency (%)
shooting24871
 
4.1%
personal18183
 
3.0%
offensive3810
 
0.6%
loose ball3192
 
0.5%
personal take1647
 
0.3%
technical1006
 
0.2%
offensive charge896
 
0.1%
shooting block590
 
0.1%
personal block490
 
0.1%
def 3 sec tech346
 
0.1%
Other values (4)307
 
0.1%
(Missing)552823
90.9%

Length

2022-01-05T15:24:15.051903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shooting25461
40.1%
personal20320
32.0%
offensive4706
 
7.4%
loose3192
 
5.0%
ball3192
 
5.0%
take1647
 
2.6%
block1080
 
1.7%
technical1006
 
1.6%
charge896
 
1.4%
sec346
 
0.5%
Other values (10)1620
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fouler
Categorical

HIGH CARDINALITY
MISSING

Distinct944
Distinct (%)1.7%
Missing552823
Missing (%)90.9%
Memory size4.6 MiB
G. Antetokounmpo - antetgi01
 
290
D. Green - greendr01
 
281
J. Harden - hardeja01
 
280
A. Drummond - drumman01
 
275
S. Ibaka - ibakase01
 
274
Other values (939)
53938 

Length

Max length33
Median length21
Mean length21.49067549
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)0.1%

Sample

1st rowT. Prince - princta02
2nd rowJ. Wiseman - wisemja01
3rd rowK. Irving - irvinky01
4th rowK. Durant - duranke01
5th rowJ. Toscano-Anderson - toscaju01

Common Values

ValueCountFrequency (%)
G. Antetokounmpo - antetgi01290
 
< 0.1%
D. Green - greendr01281
 
< 0.1%
J. Harden - hardeja01280
 
< 0.1%
A. Drummond - drumman01275
 
< 0.1%
S. Ibaka - ibakase01274
 
< 0.1%
P. Tucker - tuckepj01267
 
< 0.1%
M. Plumlee - plumlma01265
 
< 0.1%
N. Jokić - jokicni01262
 
< 0.1%
M. Morris - morrima02260
 
< 0.1%
D. Howard - howardw01257
 
< 0.1%
Other values (934)52627
 
8.7%
(Missing)552823
90.9%

Length

2022-01-05T15:24:15.408345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
55338
25.0%
j8509
 
3.8%
d6663
 
3.0%
m4861
 
2.2%
t4275
 
1.9%
k4106
 
1.9%
a3599
 
1.6%
r3134
 
1.4%
c2509
 
1.1%
b2381
 
1.1%
Other values (1688)126131
56.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fouled
Categorical

HIGH CARDINALITY
MISSING

Distinct867
Distinct (%)1.7%
Missing556053
Missing (%)91.4%
Memory size4.6 MiB
J. Harden - hardeja01
 
592
G. Antetokounmpo - antetgi01
 
549
A. Davis - davisan02
 
508
L. James - jamesle01
 
487
D. Lillard - lillada01
 
458
Other values (862)
49514 

Length

Max length33
Median length21
Mean length21.49397405
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.2%

Sample

1st rowA. Wiggins - wiggian01
2nd rowK. Durant - duranke01
3rd rowJ. Toscano-Anderson - toscaju01
4th rowJ. Wiseman - wisemja01
5th rowD. Jordan - jordade01

Common Values

ValueCountFrequency (%)
J. Harden - hardeja01592
 
0.1%
G. Antetokounmpo - antetgi01549
 
0.1%
A. Davis - davisan02508
 
0.1%
L. James - jamesle01487
 
0.1%
D. Lillard - lillada01458
 
0.1%
D. DeRozan - derozde01458
 
0.1%
R. Westbrook - westbru01450
 
0.1%
J. Butler - butleji01407
 
0.1%
R. Gobert - goberru01400
 
0.1%
P. George - georgpa01390
 
0.1%
Other values (857)47409
 
7.8%
(Missing)556053
91.4%

Length

2022-01-05T15:24:15.548181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
52108
25.0%
j8102
 
3.9%
d7113
 
3.4%
k4343
 
2.1%
m4069
 
2.0%
t3437
 
1.6%
a3349
 
1.6%
r2841
 
1.4%
b2620
 
1.3%
c2196
 
1.1%
Other values (1557)118357
56.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rebounder
Categorical

HIGH CARDINALITY
MISSING

Distinct936
Distinct (%)0.7%
Missing470175
Missing (%)77.3%
Memory size4.6 MiB
Team
21662 
A. Drummond - drumman01
 
1172
D. Jordan - jordade01
 
1055
H. Whiteside - whiteha01
 
915
R. Gobert - goberru01
 
911
Other values (931)
112271 

Length

Max length33
Median length21
Mean length18.73998811
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st rowK. Irving - irvinky01
2nd rowTeam
3rd rowJ. Harris - harrijo01
4th rowK. Oubre - oubreke01
5th rowK. Oubre - oubreke01

Common Values

ValueCountFrequency (%)
Team21662
 
3.6%
A. Drummond - drumman011172
 
0.2%
D. Jordan - jordade011055
 
0.2%
H. Whiteside - whiteha01915
 
0.2%
R. Gobert - goberru01911
 
0.1%
G. Antetokounmpo - antetgi01904
 
0.1%
N. Jokić - jokicni01867
 
0.1%
K. Towns - townska01853
 
0.1%
A. Davis - davisan02832
 
0.1%
L. James - jamesle01771
 
0.1%
Other values (926)108044
 
17.8%
(Missing)470175
77.3%

Length

2022-01-05T15:24:15.704446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
116324
23.9%
team21662
 
4.4%
j16795
 
3.4%
d14098
 
2.9%
m9376
 
1.9%
t9104
 
1.9%
k8890
 
1.8%
a8403
 
1.7%
r6467
 
1.3%
b5275
 
1.1%
Other values (1672)270845
55.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reboundtype
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing470175
Missing (%)77.3%
Memory size4.6 MiB
defensive
95141 
offensive
42845 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdefensive
2nd rowoffensive
3rd rowoffensive
4th rowoffensive
5th rowoffensive

Common Values

ValueCountFrequency (%)
defensive95141
 
15.6%
offensive42845
 
7.0%
(Missing)470175
77.3%

Length

2022-01-05T15:24:15.837142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:15.920823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
defensive95141
68.9%
offensive42845
31.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

violationplayer
Categorical

HIGH CARDINALITY
MISSING

Distinct346
Distinct (%)16.0%
Missing605996
Missing (%)99.6%
Memory size4.6 MiB
Team
1145 
K. Towns - townska01
 
17
M. Plumlee - plumlma01
 
16
D. Jordan - jordade01
 
15
G. Dieng - dienggo01
 
14
Other values (341)
958 

Length

Max length31
Median length4
Mean length12.18752887
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)6.3%

Sample

1st rowTeam
2nd rowTeam
3rd rowTeam
4th rowTeam
5th rowTeam

Common Values

ValueCountFrequency (%)
Team1145
 
0.2%
K. Towns - townska0117
 
< 0.1%
M. Plumlee - plumlma0116
 
< 0.1%
D. Jordan - jordade0115
 
< 0.1%
G. Dieng - dienggo0114
 
< 0.1%
J. Henson - hensojo0114
 
< 0.1%
D. Howard - howardw0113
 
< 0.1%
A. Drummond - drumman0112
 
< 0.1%
P. Patterson - pattepa0112
 
< 0.1%
W. Barton - bartowi0112
 
< 0.1%
Other values (336)895
 
0.1%
(Missing)605996
99.6%

Length

2022-01-05T15:24:16.009075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
team1145
21.9%
1020
19.5%
j137
 
2.6%
d125
 
2.4%
m100
 
1.9%
k88
 
1.7%
a69
 
1.3%
t65
 
1.2%
s55
 
1.1%
r51
 
1.0%
Other values (664)2372
45.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

violationtype
Categorical

MISSING

Distinct6
Distinct (%)0.3%
Missing605996
Missing (%)99.6%
Memory size4.6 MiB
kicked ball
1037 
def goaltending
617 
delay of game
313 
jump ball
152 
lane
 
37

Length

Max length15
Median length11
Mean length12.16905312
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkicked ball
2nd rowdelay of game
3rd rowdef goaltending
4th rowdef goaltending
5th rowdelay of game

Common Values

ValueCountFrequency (%)
kicked ball1037
 
0.2%
def goaltending617
 
0.1%
delay of game313
 
0.1%
jump ball152
 
< 0.1%
lane37
 
< 0.1%
double lane9
 
< 0.1%
(Missing)605996
99.6%

Length

2022-01-05T15:24:16.215761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:16.318009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ball1189
25.8%
kicked1037
22.5%
def617
13.4%
goaltending617
13.4%
delay313
 
6.8%
of313
 
6.8%
game313
 
6.8%
jump152
 
3.3%
lane46
 
1.0%
double9
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

timeoutteam
Categorical

HIGH CORRELATION
MISSING

Distinct30
Distinct (%)0.2%
Missing592310
Missing (%)97.4%
Memory size4.6 MiB
MEM
 
572
PHI
 
565
TOR
 
565
DEN
 
561
OKC
 
557
Other values (25)
13031 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLAL
2nd rowLAC
3rd rowCHO
4th rowCHO
5th rowCLE

Common Values

ValueCountFrequency (%)
MEM572
 
0.1%
PHI565
 
0.1%
TOR565
 
0.1%
DEN561
 
0.1%
OKC557
 
0.1%
GSW554
 
0.1%
MIA554
 
0.1%
IND553
 
0.1%
DAL553
 
0.1%
MIN551
 
0.1%
Other values (20)10266
 
1.7%
(Missing)592310
97.4%

Length

2022-01-05T15:24:16.644734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mem572
 
3.6%
phi565
 
3.6%
tor565
 
3.6%
den561
 
3.5%
okc557
 
3.5%
gsw554
 
3.5%
mia554
 
3.5%
ind553
 
3.5%
dal553
 
3.5%
min551
 
3.5%
Other values (20)10266
64.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

freethrowshooter
Categorical

HIGH CARDINALITY
MISSING

Distinct873
Distinct (%)1.4%
Missing547711
Missing (%)90.1%
Memory size4.6 MiB
J. Harden - hardeja01
 
934
G. Antetokounmpo - antetgi01
 
709
D. DeRozan - derozde01
 
657
R. Westbrook - westbru01
 
621
L. James - jamesle01
 
612
Other values (868)
56917 

Length

Max length33
Median length21
Mean length21.51629446
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)0.1%

Sample

1st rowA. Wiggins - wiggian01
2nd rowT. Prince - princta02
3rd rowS. Dinwiddie - dinwisp01
4th rowK. Durant - duranke01
5th rowD. Jordan - jordade01

Common Values

ValueCountFrequency (%)
J. Harden - hardeja01934
 
0.2%
G. Antetokounmpo - antetgi01709
 
0.1%
D. DeRozan - derozde01657
 
0.1%
R. Westbrook - westbru01621
 
0.1%
L. James - jamesle01612
 
0.1%
J. Butler - butleji01597
 
0.1%
A. Davis - davisan02583
 
0.1%
D. Lillard - lillada01580
 
0.1%
K. Durant - duranke01502
 
0.1%
K. Leonard - leonaka01480
 
0.1%
Other values (863)54175
 
8.9%
(Missing)547711
90.1%

Length

2022-01-05T15:24:17.053896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60450
25.0%
j9551
 
3.9%
d8297
 
3.4%
k5316
 
2.2%
m4350
 
1.8%
t3912
 
1.6%
a3790
 
1.6%
r3297
 
1.4%
b3068
 
1.3%
c2556
 
1.1%
Other values (1566)137341
56.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

freethrowoutcome
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing547711
Missing (%)90.1%
Memory size4.6 MiB
make
46379 
miss
14071 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmake
2nd rowmiss
3rd rowmake
4th rowmake
5th rowmiss

Common Values

ValueCountFrequency (%)
make46379
 
7.6%
miss14071
 
2.3%
(Missing)547711
90.1%

Length

2022-01-05T15:24:17.604809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:17.692663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
make46379
76.7%
miss14071
 
23.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

freethrownum
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing547711
Missing (%)90.1%
Memory size4.6 MiB
1 of 2
25107 
2 of 2
24763 
1 of 1
6093 
technical
 
1441
3 of 3
 
1039
Other values (2)
 
2007

Length

Max length9
Median length6
Mean length6.071513648
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2 of 2
2nd row1 of 2
3rd row1 of 2
4th row2 of 2
5th row2 of 2

Common Values

ValueCountFrequency (%)
1 of 225107
 
4.1%
2 of 224763
 
4.1%
1 of 16093
 
1.0%
technical1441
 
0.2%
3 of 31039
 
0.2%
2 of 31008
 
0.2%
1 of 3999
 
0.2%
(Missing)547711
90.1%

Length

2022-01-05T15:24:17.772525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:17.932717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
275641
42.4%
of59009
33.1%
138292
21.5%
34085
 
2.3%
technical1441
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

entergame
Categorical

HIGH CARDINALITY
MISSING

Distinct969
Distinct (%)1.6%
Missing547406
Missing (%)90.0%
Memory size4.6 MiB
P. Mills - millspa02
 
305
J. Ingles - inglejo01
 
272
C. Joseph - josepco01
 
270
A. Iguodala - iguodan01
 
253
M. Smart - smartma01
 
250
Other values (964)
59405 

Length

Max length33
Median length21
Mean length21.50341536
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.1%

Sample

1st rowJ. Allen - allenja01
2nd rowS. Curry - curryst01
3rd rowJ. Toscano-Anderson - toscaju01
4th rowM. Chriss - chrisma01
5th rowA. Wiggins - wiggian01

Common Values

ValueCountFrequency (%)
P. Mills - millspa02305
 
0.1%
J. Ingles - inglejo01272
 
< 0.1%
C. Joseph - josepco01270
 
< 0.1%
A. Iguodala - iguodan01253
 
< 0.1%
M. Smart - smartma01250
 
< 0.1%
T. McConnell - mccontj01247
 
< 0.1%
E. İlyasova - ilyaser01240
 
< 0.1%
K. Korver - korveky01234
 
< 0.1%
M. Belinelli - belinma01231
 
< 0.1%
L. Williams - willilo02231
 
< 0.1%
Other values (959)58222
 
9.6%
(Missing)547406
90.0%

Length

2022-01-05T15:24:18.208380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60755
25.0%
j9194
 
3.8%
d6999
 
2.9%
m5401
 
2.2%
t5300
 
2.2%
a4001
 
1.6%
k3965
 
1.6%
r3168
 
1.3%
c3008
 
1.2%
s2638
 
1.1%
Other values (1731)138870
57.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

leavegame
Categorical

HIGH CARDINALITY
MISSING

Distinct887
Distinct (%)1.5%
Missing547406
Missing (%)90.0%
Memory size4.6 MiB
A. Horford - horfoal01
 
290
D. Green - greenda02
 
284
G. Antetokounmpo - antetgi01
 
283
B. Lopez - lopezbr01
 
280
C. McCollum - mccolcj01
 
280
Other values (882)
59338 

Length

Max length33
Median length21
Mean length21.50654267
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.1%

Sample

1st rowD. Jordan - jordade01
2nd rowA. Wiggins - wiggian01
3rd rowB. Wanamaker - wanambr01
4th rowK. Looney - looneke01
5th rowK. Oubre - oubreke01

Common Values

ValueCountFrequency (%)
A. Horford - horfoal01290
 
< 0.1%
D. Green - greenda02284
 
< 0.1%
G. Antetokounmpo - antetgi01283
 
< 0.1%
B. Lopez - lopezbr01280
 
< 0.1%
C. McCollum - mccolcj01280
 
< 0.1%
J. Ingles - inglejo01275
 
< 0.1%
C. Capela - capelca01273
 
< 0.1%
S. Adams - adamsst01271
 
< 0.1%
J. Redick - redicjj01271
 
< 0.1%
N. Jokić - jokicni01260
 
< 0.1%
Other values (877)57988
 
9.5%
(Missing)547406
90.0%

Length

2022-01-05T15:24:18.336929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60755
25.0%
j9192
 
3.8%
d7480
 
3.1%
t5013
 
2.1%
m4989
 
2.1%
k4294
 
1.8%
a3981
 
1.6%
r3361
 
1.4%
c2853
 
1.2%
b2748
 
1.1%
Other values (1592)138637
57.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

turnoverplayer
Categorical

HIGH CARDINALITY
MISSING

Distinct845
Distinct (%)2.5%
Missing574146
Missing (%)94.4%
Memory size4.6 MiB
Team
 
1532
R. Westbrook - westbru01
 
369
J. Harden - hardeja01
 
347
L. James - jamesle01
 
322
G. Antetokounmpo - antetgi01
 
242
Other values (840)
31203 

Length

Max length33
Median length21
Mean length20.65244745
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.2%

Sample

1st rowK. Oubre - oubreke01
2nd rowK. Oubre - oubreke01
3rd rowA. Wiggins - wiggian01
4th rowD. Jordan - jordade01
5th rowD. Jordan - jordade01

Common Values

ValueCountFrequency (%)
Team1532
 
0.3%
R. Westbrook - westbru01369
 
0.1%
J. Harden - hardeja01347
 
0.1%
L. James - jamesle01322
 
0.1%
G. Antetokounmpo - antetgi01242
 
< 0.1%
D. Green - greendr01240
 
< 0.1%
S. Curry - curryst01239
 
< 0.1%
D. Lillard - lillada01227
 
< 0.1%
P. George - georgpa01212
 
< 0.1%
J. Wall - walljo01208
 
< 0.1%
Other values (835)30077
 
4.9%
(Missing)574146
94.4%

Length

2022-01-05T15:24:18.596319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
32483
24.7%
j5089
 
3.9%
d4140
 
3.1%
k2623
 
2.0%
m2484
 
1.9%
t2373
 
1.8%
r1959
 
1.5%
a1864
 
1.4%
b1629
 
1.2%
team1532
 
1.2%
Other values (1518)75364
57.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

turnovertype
Categorical

HIGH CARDINALITY
MISSING

Distinct453
Distinct (%)1.2%
Missing570756
Missing (%)93.8%
Memory size4.6 MiB
bad pass
15310 
lost ball
6925 
offensive foul
4606 
traveling
2398 
shot clock
 
1475
Other values (448)
6691 

Length

Max length33
Median length9
Mean length11.05223901
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)0.1%

Sample

1st rowoffensive foul
2nd rowbad pass
3rd rowtraveling
4th rowoffensive foul
5th rowlost ball

Common Values

ValueCountFrequency (%)
bad pass15310
 
2.5%
lost ball6925
 
1.1%
offensive foul4606
 
0.8%
traveling2398
 
0.4%
shot clock1475
 
0.2%
out of bounds lost ball1157
 
0.2%
step out of bounds852
 
0.1%
turnover313
 
0.1%
3 sec277
 
< 0.1%
back court165
 
< 0.1%
Other values (443)3927
 
0.6%
(Missing)570756
93.8%

Length

2022-01-05T15:24:19.280111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bad15310
18.2%
pass15310
18.2%
ball8126
9.7%
lost8082
9.6%
offensive4702
 
5.6%
foul4606
 
5.5%
3363
 
4.0%
traveling2398
 
2.9%
of2009
 
2.4%
bounds2009
 
2.4%
Other values (840)18002
21.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

turnovercause
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing587928
Missing (%)96.7%
Memory size4.6 MiB
steal
16870 
bad pass
2141 
lost ball
 
1221
turnover
 
1

Length

Max length9
Median length5
Mean length5.558987792
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsteal
2nd rowsteal
3rd rowsteal
4th rowsteal
5th rowsteal

Common Values

ValueCountFrequency (%)
steal16870
 
2.8%
bad pass2141
 
0.4%
lost ball1221
 
0.2%
turnover1
 
< 0.1%
(Missing)587928
96.7%

Length

2022-01-05T15:24:19.447918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-05T15:24:19.531962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
steal16870
71.5%
bad2141
 
9.1%
pass2141
 
9.1%
lost1221
 
5.2%
ball1221
 
5.2%
turnover1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

turnovercauser
Categorical

HIGH CARDINALITY
MISSING

Distinct766
Distinct (%)3.8%
Missing587928
Missing (%)96.7%
Memory size4.6 MiB
steal
3363 
P. George - georgpa01
 
145
R. Westbrook - westbru01
 
134
S. Curry - curryst01
 
130
D. Green - greendr01
 
123
Other values (761)
16338 

Length

Max length33
Median length21
Mean length18.70775466
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)0.5%

Sample

1st rowJ. Green - greenje02
2nd rowJ. Wiseman - wisemja01
3rd rowK. Durant - duranke01
4th rowD. Jordan - jordade01
5th rowT. Johnson - johnsty01

Common Values

ValueCountFrequency (%)
steal3363
 
0.6%
P. George - georgpa01145
 
< 0.1%
R. Westbrook - westbru01134
 
< 0.1%
S. Curry - curryst01130
 
< 0.1%
D. Green - greendr01123
 
< 0.1%
C. Paul - paulch01122
 
< 0.1%
J. Harden - hardeja01118
 
< 0.1%
R. Rubio - rubiori01117
 
< 0.1%
P. Tucker - tuckepj01113
 
< 0.1%
M. Smart - smartma01109
 
< 0.1%
Other values (756)15759
 
2.6%
(Missing)587928
96.7%

Length

2022-01-05T15:24:19.671392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16870
23.8%
steal3363
 
4.7%
j2486
 
3.5%
d1946
 
2.7%
t1423
 
2.0%
m1373
 
1.9%
k1335
 
1.9%
a1048
 
1.5%
r1020
 
1.4%
s753
 
1.1%
Other values (1380)39317
55.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

jumpballawayplayer
Categorical

HIGH CARDINALITY
MISSING

Distinct559
Distinct (%)10.1%
Missing602617
Missing (%)99.1%
Memory size4.6 MiB
S. Adams - adamsst01
 
96
A. Drummond - drumman01
 
88
A. Davis - davisan02
 
67
M. Gasol - gasolma01
 
66
B. Lopez - lopezbr01
 
57
Other values (554)
5170 

Length

Max length33
Median length21
Mean length21.47276335
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)1.8%

Sample

1st rowC. Sexton - sextoco01
2nd rowT. Bryant - bryanth01
3rd rowM. Bagley - baglema01
4th rowS. Ibaka - ibakase01
5th rowA. Drummond - drumman01

Common Values

ValueCountFrequency (%)
S. Adams - adamsst0196
 
< 0.1%
A. Drummond - drumman0188
 
< 0.1%
A. Davis - davisan0267
 
< 0.1%
M. Gasol - gasolma0166
 
< 0.1%
B. Lopez - lopezbr0157
 
< 0.1%
N. Jokić - jokicni0156
 
< 0.1%
R. Gobert - goberru0153
 
< 0.1%
A. Horford - horfoal0150
 
< 0.1%
C. Capela - capelca0148
 
< 0.1%
J. Butler - butleji0148
 
< 0.1%
Other values (549)4915
 
0.8%
(Missing)602617
99.1%

Length

2022-01-05T15:24:19.799848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5544
25.0%
j755
 
3.4%
d681
 
3.1%
m505
 
2.3%
a453
 
2.0%
t407
 
1.8%
k390
 
1.8%
b317
 
1.4%
r311
 
1.4%
c249
 
1.1%
Other values (1040)12568
56.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

jumpballhomeplayer
Categorical

HIGH CARDINALITY
MISSING

Distinct398
Distinct (%)18.2%
Missing605980
Missing (%)99.6%
Memory size4.6 MiB
K. Towns - townska01
 
66
J. Valančiūnas - valanjo01
 
61
N. Vučević - vucevni01
 
51
M. Turner - turnemy01
 
51
H. Whiteside - whiteha01
 
44
Other values (393)
1908 

Length

Max length33
Median length21
Mean length21.42457588
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)7.4%

Sample

1st rowP. Washington - washipj01
2nd rowJ. Embiid - embiijo01
3rd rowN. Jokić - jokicni01
4th rowN. Jokić - jokicni01
5th rowM. Plumlee - plumlma01

Common Values

ValueCountFrequency (%)
K. Towns - townska0166
 
< 0.1%
J. Valančiūnas - valanjo0161
 
< 0.1%
N. Vučević - vucevni0151
 
< 0.1%
M. Turner - turnemy0151
 
< 0.1%
H. Whiteside - whiteha0144
 
< 0.1%
T. Thompson - thomptr0143
 
< 0.1%
D. Jordan - jordade0142
 
< 0.1%
M. Plumlee - plumlma0140
 
< 0.1%
R. Lopez - lopezro0140
 
< 0.1%
A. Drummond - drumman0139
 
< 0.1%
Other values (388)1704
 
0.3%
(Missing)605980
99.6%

Length

2022-01-05T15:24:19.922104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2181
25.0%
j337
 
3.9%
d233
 
2.7%
m209
 
2.4%
a190
 
2.2%
t163
 
1.9%
k160
 
1.8%
r132
 
1.5%
n123
 
1.4%
b105
 
1.2%
Other values (749)4891
56.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

jumpballposs
Categorical

HIGH CARDINALITY
MISSING

Distinct473
Distinct (%)21.7%
Missing605980
Missing (%)99.6%
Memory size4.6 MiB
R. Jackson - jacksre01
 
32
G. Antetokounmpo - antetgi01
 
28
T. Harris - harrito02
 
27
T. Gibson - gibsota01
 
26
S. Ibaka - ibakase01
 
25
Other values (468)
2043 

Length

Max length33
Median length21
Mean length21.51169188
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)6.6%

Sample

1st rowA. Drummond - drumman01
2nd rowS. Curry - curryse01
3rd rowJ. Murray - murraja01
4th rowP. Millsap - millspa01
5th rowD. Rose - rosede01

Common Values

ValueCountFrequency (%)
R. Jackson - jacksre0132
 
< 0.1%
G. Antetokounmpo - antetgi0128
 
< 0.1%
T. Harris - harrito0227
 
< 0.1%
T. Gibson - gibsota0126
 
< 0.1%
S. Ibaka - ibakase0125
 
< 0.1%
D. Schröder - schrode0125
 
< 0.1%
C. Paul - paulch0124
 
< 0.1%
D. Green - greendr0123
 
< 0.1%
B. Simmons - simmobe0122
 
< 0.1%
R. Westbrook - westbru0121
 
< 0.1%
Other values (463)1928
 
0.3%
(Missing)605980
99.6%

Length

2022-01-05T15:24:20.257950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2181
25.0%
d319
 
3.7%
j307
 
3.5%
t196
 
2.2%
m163
 
1.9%
k153
 
1.8%
r133
 
1.5%
l106
 
1.2%
a102
 
1.2%
s97
 
1.1%
Other values (883)4968
56.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-05T15:23:39.514785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:09.782713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:17.256541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:41.266218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:10.691740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:20.870473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:49.624653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:15.786272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-05T15:23:33.581706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-05T15:24:20.394065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-05T15:23:52.931850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-05T15:23:56.639091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-05T15:24:04.357214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-05T15:24:07.118038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

urlgametypelocationdatetimewinningteamquartersecleftawayteamawayplayawayscorehometeamhomeplayhomescoreshootershottypeshotoutcomeshotdistassisterblockerfoultypefoulerfouledrebounderreboundtypeviolationplayerviolationtypetimeoutteamfreethrowshooterfreethrowoutcomefreethrownumentergameleavegameturnoverplayerturnovertypeturnovercauseturnovercauserjumpballawayplayerjumpballhomeplayerjumpballposs
0/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1682GSWNone2BRKK. Irving makes 2-pt jump shot from 22 ft (assist by K. Durant)2K. Irving - irvinky012-pt jump shotmake22.0K. Durant - duranke01NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
1/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1591GSWNone4BRKDefensive rebound by K. Irving10NoneNoneNoneNoneNoneNoneNoneNoneNoneK. Irving - irvinky01defensiveNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
2/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1589GSWNone4BRKS. Dinwiddie misses 3-pt jump shot from 26 ft10S. Dinwiddie - dinwisp013-pt jump shotmiss26.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
3/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1550GSWNone6BRKK. Irving misses 3-pt jump shot from 25 ft10K. Irving - irvinky013-pt jump shotmiss25.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
4/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1496GSWNone8BRKOffensive rebound by Team13NoneNoneNoneNoneNoneNoneNoneNoneNoneTeamoffensiveNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
5/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1489GSWNone8BRKOffensive rebound by J. Harris13NoneNoneNoneNoneNoneNoneNoneNoneNoneJ. Harris - harrijo01offensiveNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
6/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1477GSWTurnover by K. Oubre (offensive foul)8BRKNone16NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneK. Oubre - oubreke01offensive foulNoneNoneNoneNoneNone
7/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1375GSWS. Curry misses 3-pt jump shot from 25 ft9BRKNone23S. Curry - curryst013-pt jump shotmiss25.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
8/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1360GSWNone9BRKJ. Allen enters the game for D. Jordan23NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneJ. Allen - allenja01D. Jordan - jordade01NoneNoneNoneNoneNoneNoneNone
9/boxscores/202012220BRK.htmlregularBarclays Center Brooklyn New YorkDecember 22 20207:00 PMBRK1325GSWNone9BRKJ. Harris misses 3-pt jump shot from 25 ft23J. Harris - harrijo013-pt jump shotmiss25.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone

Last rows

urlgametypelocationdatetimewinningteamquartersecleftawayteamawayplayawayscorehometeamhomeplayhomescoreshootershottypeshotoutcomeshotdistassisterblockerfoultypefoulerfouledrebounderreboundtypeviolationplayerviolationtypetimeoutteamfreethrowshooterfreethrowoutcomefreethrownumentergameleavegameturnoverplayerturnovertypeturnovercauseturnovercauserjumpballawayplayerjumpballhomeplayerjumpballposs
608151/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW4494CLENone102GSWK. Durant makes 2-pt dunk at rim (assist by A. Iguodala)110K. Durant - duranke012-pt dunkmake0.0A. Iguodala - iguodan01NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608152/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW4470CLEL. James misses 2-pt jump shot from 9 ft102GSWNone110L. James - jamesle012-pt jump shotmiss9.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608153/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW4301CLENone106GSWK. Durant makes 2-pt layup from 2 ft (assist by S. Curry)118K. Durant - duranke012-pt layupmake2.0S. Curry - curryst01NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608154/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW4227CLENone108GSWK. Durant misses 2-pt jump shot from 16 ft122K. Durant - duranke012-pt jump shotmiss16.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608155/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW4218CLEL. James makes 2-pt layup from 1 ft110GSWNone122L. James - jamesle012-pt layupmake1.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608156/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW4179CLEJ. Smith makes 3-pt jump shot from 26 ft113GSWNone124J. Smith - smithjr013-pt jump shotmake26.0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608157/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW4111CLENone115GSWDefensive rebound by D. Green126NoneNoneNoneNoneNoneNoneNoneNoneNoneD. Green - greendr01defensiveNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608158/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW465CLENone115GSWK. Durant misses 3-pt jump shot from 26 ft (block by L. James)126K. Durant - duranke013-pt jump shotmiss26.0NoneL. James - jamesle01NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608159/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW463CLEDefensive rebound by L. James115GSWNone126NoneNoneNoneNoneNoneNoneNoneNoneNoneL. James - jamesle01defensiveNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone
608160/boxscores/201706120GSW.htmlplayoffOracle Arena Oakland CaliforniaJune 12 20179:00 PMGSW420CLEDefensive rebound by T. Thompson117GSWNone129NoneNoneNoneNoneNoneNoneNoneNoneNoneT. Thompson - thomptr01defensiveNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone